System and method for assessment of health risks and visualization of weight loss and muscle gain

ABSTRACT

The present system combines image morphing technology, exercise programming, supplement sales, and motivational techniques into one product. Users begin by entering their current measurements, measurement goals and current picture into the system, preferably via a Web site. The picture is segmented into body components, and each affected segment is morphed based upon the measurements, goals, and the segment&#39;s responsiveness to weight loss in order to create a modified image representative of the user in a post-regimen condition. This system helps health and fitness businesses obtain new members and retain existing members by showing the members how they will look after following a specific regimen of diet and/or exercise. The system also predicts health risks of diabetes, heart disease, and stroke associated with the user&#39;s pre-regimen and post-regimen conditions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/897,616 filed Oct. 4, 2010, now U.S. Pat. No. 8,094,878 which is acontinuation of U.S. patent application Ser. No. 10/684,023 filed Oct.10, 2003, now U.S. Pat. No. 7,809,153, which is a continuation-in-partof U.S. application Ser. No. 09/560,243 filed on Apr. 27, 2000, now U.S.Pat. No. 6,643,385, each of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to visual image processing used to predict asubject's appearance after a given amount of weight loss or muscle gainand prediction of changes in health risks associated with such weightloss or muscle gain. This invention also relates to business methodsemploying a predictive image visualization system to attract and retainclients of service providers in the weight loss food program, fitnesscenter, physical therapy, sports medicine, and weight control medicalindustries.

2. Description of the Related Art

Many people desire to decrease their body weight, especially their bodyfat content. Modern life styles include highly sedentary weekdayroutines such as computer-based desk jobs, low-exercise commutingroutines such as transportation by private automobile, and high-fat,high-calorie food selections often eaten quickly or while “on the run.”Besides genetic tendencies, these factors lead many people to bedissatisfied with their appearance.

The problem is so prevalent that billion dollar industries have evolvedto help people overcome their body dissatisfaction, including packagedfood programs such as Weight Watchers™ and Jenny Craig™, fitness andworkout centers such as Bally's™, and physical therapy and sportsmedicine centers. This industry has also attracted medical andosteopathic doctors who specialize in the use of diet, exercise, andsometimes prescriptive regimens to help their clients and patientsachieve their weight and appearance goals.

According to marketLooks.com™, there are currently over 24,000 healthclubs in the United States with 40 million members generating over 12billion dollars in revenue each year. In 1995, health clubs and privateindividuals spent 3.2 billion dollars on fitness equipment alone, andthese revenues are expected to reach 4.9 billion, a 38% increase, by theyear 2001. In 1996, $500 million was spent on meal replacements andprotein drinks, and these sales are expected to grow by 30% over thenext five years.

However, many people fail to meet their goals, despite their efforts andthe amounts they spend. The two most common reasons people fail in theirattempts to change their body weight and appearance are lack ofunderstanding and motivation.

Client and Patient Education

Previous technologies, systems, and methods do not adequately providefor the education and understanding of how exercise and diet affect thephysiology of a person, especially taking into consideration theperson's frame size or “build” and metabolism. Some availabletechnologies include the ability to scan a photograph or import an imageof a client or patient from a digital camera and to digitally alter theimage manually to produce an estimate of the client's future appearance.

Currently available systems and methods simply “shrink” an image, suchas by hand manipulation and editing of a digitized photograph, alsoknown as digital “retouching.” However, different body builds will storefat in different amounts in various portions of the body, and differentexercises will reduce and/or firm up different body areas unevenly.Additionally, certain features of the body will show little or noresponse to weight change. For example, if the width of an image of aleg is decreased by a certain percentage, the appearance of the kneewill be changed. However, knees generally do not have a significant fatlayer and thus have a minimum circumference at almost any weight. So,the resulting image would predict an overall thin appearance to a legwhich is not physiologically achievable. Similar factors apply to otherpoints in the body, such as the width of shoulders and hips and thecircumference of joints. As this method is highly inaccurate, it doesnot provide the level of education a client or patient needs tounderstand why particular diets and exercises have been recommended andhow to adjust and apply this information in the future.

In order to accurately predict a future appearance, many physiologicalfactors must be taken into account with diet and exercise goals.Estimating the results of these changes is typically beyond thetechnical and medical education and skill sets of most staffers atweight loss packaged food program outlets and physical fitness centers,and such estimation may be highly labor intensive and expensive ifperformed by appropriately qualified health and medical professionals.

At present, there are a few relevant resources available on theInternet. One service, called MorphOver™ from eFit of New York City,N.Y., provides a service in which users e-mail a digital photograph inJPEG format to the company's website without any body measurements, bodyfat data, or indicated goals, and the service returns a “slimmed”photograph file within a few weeks. The instructions indicate that theoriginal or “before” photograph must be of the subject in dark clothing,in a certain position, and with a white background. Another on-lineservice offered by Sound Feelings Publishing of Reseda, Calif., issimilar in that it only requires submission of a photograph without anydata as to the subject's body fat, dimensions, or goals. Additionally,the advertisement for this service states that a digital photographartist will spend at least two hours manually manipulating thephotograph.

Client and Patient Motivation

There are very few credible, non-surgical remedies for rapid weightloss. Therefore, successful weight loss programs require months to evenyears of commitment and adherence to prescribed diet and exerciseregimens. If a client or patient becomes unmotivated or loses confidencein a program, he or she will not continue the program. Further, thisclient or patient may produce negative effects on the attraction andretention of other clients and patients as they will report to theirfriends and acquaintances that the program is another “scam” or “doesn'twork,” or that a particular professional is not competent. This can leadto a decline in memberships of businesses which are membership-based.

Therefore, there is a need in the art for a system and method whichaccurately produces predicted images of a weight-loss client or patient.Such a system and method should be operable by persons of usual skilland education who are commonly employed in the package food program andfitness center industries. Further, there is a need in the art for thissystem and method to easily and quickly produce intermediate images,such as weekly or monthly predictions, in order to provide accurate andpositive confidence reinforcement to the client or patient, therebyenhancing the likelihood that the client or patient will continue toabide by the program and ultimately achieve his or her goals. There alsoexists a need in the art for this system and method to be operable in anetworked or an Internet-based form or in a single workstation form.Additionally, there exists a need in the art for a method of leveragingsuch a system to attract and retain clients and patients in thisindustry.

SUMMARY OF THE INVENTION

System Overview

The present system comprises a fitness profiler which helps users gaininsight into their fitness plans and their projected outcomes andresults. The present system combines image morphing technology, exerciseprogramming, supplement sales, and motivational techniques into oneproduct.

Users begin by entering their measurement goals and current picture intothe system, preferably via an Internet web site. The system analyzes theuser's data and produces a customized fitness plan by applying a“morphing” process to the “before view.” The picture is sectionalizedinto body components which are highly responsive to weight loss andcomponents which are less responsive to weight loss, and the amount ofchange in each body section is determined by physiological tables andformulae. The resulting modified “after view” image is then returned tothe user, preferably by online communications such as e-mail.

The Image Analyzer

The combination of three-dimensional (“3-D”) morphing technology withmathematical statistics is used to project fat loss and muscle gain andto produce projected fitness outcomes. The user's input data preferablyincludes skin fold, circumference, height, weight, BMR, and activitylevel. By entering the client's measurements into a mathematicalformula, the user's picture can be morphed into the desired outcome. Thecombination of skin fold and circumference measurement produces anaccurate morphing outcome for each user.

Business Method for Use of the Predictive System

The image prediction system in accordance with the present inventionhelps the fitness industry overcome two of their biggest problems:obtaining new members and retaining current members. People may decideto join or renew their membership with a specific health club becausethey offer the present image prediction system as a service. By showingmembers how they will look 10 pounds thinner, for example, and givingthem a clear-cut, understandable plan on how to achieve it, businessesin this industry will generate a satisfied and loyal clientele.

The present image prediction system is useful for nationwide healthclubs, diet centers, and exercise equipment manufacturers. Directmarketing to Internet users may also be employed, as the technology andmethods lend themselves well to interfacing to the user via common website and browser technologies. As such, Internet users who are lookingto start a fitness program may have access to the present imageprediction system via a web site.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the arrangement of an Internet browser computer, digitalphotography and scanning equipment, the Internet, and a system server inaccordance with the present invention.

FIG. 2 illustrates in detail the functional organization of the systemof FIG. 1.

FIG. 3 depicts a cross-sectional view of a body portion to illustratethe calculation of base circumference of a body part.

FIG. 4 sets forth an example of locating grids placed on a subject'sphotograph to aid the image processor in locating each body part.

FIG. 5 shows the result of the placing of a grid over a single body partduring the process of finding edges of the body part.

FIG. 6 illustrates the result of the morphing to reduce the width of thebody part image.

FIG. 7 shows a simulated side-by-side “before” and “after” comparisonoutput from the system.

FIG. 8 is a graph of male optimal weight versus height.

FIG. 9 is a graph of female optimal weight versus height.

FIG. 10 is a graph of essential body fat versus age as a function ofideal weight.

FIG. 11 is a graph of male age factor versus age.

FIG. 12 is a graph of female age factor versus age.

FIG. 13 is a graph of resistance training compliance by days.

FIG. 14 is a graph of muscle gain compensation by age.

FIG. 15 is a graph of muscle gain factor by age.

FIG. 16 is a graph of muscle gain scaling by nutritional compliance.

FIG. 17 is a screen display of personal information generated bycomputer software in accordance with the present invention.

FIG. 18 is a variation of the screen display of FIG. 17.

FIG. 19 is another variation of the screen display of FIG. 17.

FIG. 20 is a screen display of health risks generated by computersoftware in accordance with the present invention.

FIG. 21 is a screen display of health risks and an associated image fora female generated by computer software in accordance with the presentinvention.

FIG. 22 is a screen display of health risks and an associated image fora male generated by computer software in accordance with the presentinvention.

FIG. 23 is a variation of the screen display of FIG. 21.

FIG. 24 is a screen display of diet and exercise goals and an associatedimage for a male before commencement of a desired regimen.

FIG. 25 is a screen display for the male of FIG. 24 after following thedesired regimen for a period of time.

FIG. 26 is a screen display of diet and exercise goals and associatedimages for a male before commencement of a desired regimen and afterfollowing the desired regimen for a period of time.

FIG. 27 is a screen display of diet and exercise goals and an associatedimage for a female before commencement of a desired regimen.

FIG. 28 is a screen display for the female of FIG. 27 after followingthe desired regimen for a period of time.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

An image prediction system in accordance with the present invention ispreferably an Internet-based fitness system and service, which helps theuser meet his or her fitness objectives. However, it may be implementedas a stand-alone workstation for use within a health club facility ormedical professional's office.

In general, users enter their measurements, goals and current pictureinto the system. The system analyzes the user's data, generates a dailyfitness program to help the customer reach his or her goal, and producesan after-fitness program image of the user. By setting the goals at anintermediate level, intermediate results can be projected andvisualized.

The system employs readily available image morphing technology, drivenby specialized technology to sectionalize the image into body componentsand predict specific size changes based upon physiological formulae anddata tables.

System Overview

In a preferred embodiment, the user, health club advisor, or medicalprofessional may use the system via a web site using a web browser,although in an alternate embodiment he or she may use the systemdirectly. FIG. 1 illustrates the basic system components, including abrowser computer (1) with Internet access (5), and a digital camera (2)or digital scanner (4), and optionally a printer (3). The computer canbe any of several well-known and readily available systems, such asIBM-compatible personal computers running Microsoft's Windows operatingsystem equipped with a web browser software such as Microsoft's Exploreror Netscape's Navigator, and appropriately equipped with a dial-upmodem, cable modem, or Internet access via a local area networkinterface. Alternate computers, software and operating systems, such asApple iMac, Unix and Linux, may be used equally well.

The system also preferably includes a computer network (6), such as theInternet or an intranet, and a server (7). This server (7) is preferablybased upon any of the well-known, readily available Internet web serverplatforms, such as an IBM-compatible personal computer runningMicrosoft's Windows NT operating system and an Apache web server. Theuser may point his or her web browser to the address or UniversalResource Locator (“URL”) of the server (7) to access web pages andforms, such as HTML, XTML, and Common Gateway Interface (“CGI”), all ofwhich are well-known within the art. The user may transfer his or her“before” photo in the form of any of many well-known digital photographformats, such as Joint Photographic Experts Group (“JPEG”), bitmap(“BMP”) or tagged-image file format (“TIFF’), either by attachment to ane-mail message, retrieval by a Java client script (supplied by theserver), by file transfer protocol (“FTP”), or any other suitable means.

FIG. 2 shows a preferred functional organization of the server system(7), which includes a web content server (22), a mathematical analyzer(23) and an image processor (24). In a preferred embodiment, the serversystem interfaces directly to the Internet using any of the well-knownmethods, such as by modem or local area network. The image processor(24) is described in more detail infra, as is the mathematical analyzer(23). If the system is implemented as a stand-alone workstation, it mayalso include a Graphical User Interface (“GUI”) function for usercontrol and input, such as a web browser software or custom GUI program.Additionally, for stand-alone use, a digital camera or scanner may beadded to the system via a Universal Serial Bus (“USB”) port, parallelport, or other common computer interface.

In a preferred embodiment, the user accesses the server (7) via anInternet (6) arrangement, using his or her browser computer (1). The webcontent server (22) transmits web pages, such as HTML and CGI forms, tothe user to establish an account session and verify the user's identity,which are viewed and completed using a web browser (20). The user maythen enter specific goals and measurement data and submit a “before”photographic image file. The goals and measurements may be entered usinga client-side Java applet, Adobe Acrobat Portable Document Format(“PDF”), CGI form, or other suitable means, and the photo file (21) maybe uploaded to the server (7) by e-mail attachment, FTP, a client-sidescript, or other suitable means.

The user's measurements and goals are received by the mathematicalanalyzer (23), wherein certain formulae and data tables are applied todetermine the amount of exercise to achieve the weight loss goal, andthe amount of circumferential reduction in each body section.

The user's “before” image file (21) is received by the image processor(24), as are the body segment circumference changes from themathematical analyzer (23). The image processor (24) segments the photointo body sections, applies the reduction changes by morphing the photo,and produces the “after” image, which is then returned to the user bythe web content server (22), preferably via a web page or e-mailattachment.

In the alternative, stand-alone embodiment, the “after” image isreturned to the GUI (25) so that the operator and/or client or patientmay view the projected results.

In practice, the goals may be adjusted to produce the desired resultsand/or intermediate results, thereby providing a full fitness planneeded to achieve the user's or client's goals.

Mathematical Analysis

The present system preferably requires measurements to be taken in orderto produce a customized fitness plan. The desired measurements include:

(1) The circumferences of the neck, arm, chest, waist, hips, thigh, andcalf.

(2) The skin fold of the neck, biceps, triceps, chest, scapula, abdomen,low back, hip, thigh, hamstring, and calf.

(3) The user's height, weight, and age.

(4) Percent desired of fat.

By taking the skin fold and circumference measurements, the presentsystem utilizes the following new formula to find the circumference ofthe fat layer and predict the reduction in circumference for aparticular body segment (all units are preferably in centimeters unlessotherwise noted):C _(change) =C _(after) −C _(before)where C_(change) is the change in circumference of a body part,C_(after) is the final circumference of the body part after fat loss,and C_(before) is the circumference of the body part at the beginning ofthe program.

FIG. 3 shows a cross sectional view of a body part, such as an upper armor thigh, including a layer of fat with skin (30), a layer of muscle(31), and an underlying bone structure (32). The muscle and bonestructure represent the component of the body part which will not beheavily affected by fat loss. Thus, the circumference of the body partwithout fat is calculated as:

$\begin{matrix}{C_{{no}\mspace{14mu}{fat}} = {2 \cdot \pi \cdot r_{{no}\mspace{14mu}{fat}}}} \\{= {2 \cdot \pi \cdot \left\lbrack {r_{start} - {{twice}\mspace{14mu}{the}\mspace{14mu}{depth}\mspace{14mu}{of}\mspace{14mu}{fat}}} \right\rbrack}} \\{= {2 \cdot \pi \cdot \left\lbrack {{{C_{before}/2}\pi} - \left( {{2 \cdot {skin\_ fold}}{\_ measurement}} \right)} \right\rbrack}}\end{matrix}$where C_(no fat) represents the minimum circumference of a body partwith no fat, π represents an approximation for the constant “pi,” suchas 3.14, C_(before) represents the starting circumference (currentcircumference) of the body part, and “skin_fold_measurement” is themeasurement of standard skin fold. All units are preferably incentimeters, although the formula holds for any unit of measure.

The body part circumference after a desired percentage fat loss iscalculated as:

$\begin{matrix}{C_{after} = {2 \cdot \pi \cdot \left\{ {r_{{no}\mspace{14mu}{fat}} - {{twice}\mspace{14mu}{the}\mspace{14mu}{desired}\mspace{14mu}{depth}\mspace{14mu}{of}\mspace{14mu}{fat}}} \right\}}} \\{= {2 \cdot \pi \cdot \left\{ {\left\lbrack {r_{start} - {{twice}\mspace{14mu}{the}\mspace{14mu}{depth}\mspace{14mu}{of}\mspace{14mu}{fat}}} \right\rbrack + {{twice}\mspace{14mu}{the}\mspace{14mu}{desired}\mspace{14mu}{depth}\mspace{14mu}{of}\mspace{14mu}{fat}}} \right\}}} \\{= {2 \cdot \pi \cdot \left\{ {\left\lbrack {{{C_{before}/2}\pi} - \left( {{2 \cdot {skin\_ fold}}{\_ measurement}} \right)} \right\rbrack + \left\lbrack {{2 \cdot {skin\_ fold}}{{\_ measurement} \cdot \left( {1 - P} \right) \cdot V}} \right\rbrack} \right\}}}\end{matrix}$where C_(after) is the circumference of the body part after the desiredfat loss, P is the amount of desired fat loss expressed in decimal form(e.g., 10% desired loss would be 0.10), and V is a constant based uponthe body part being analyzed. The V constant is drawn from a table, andprovides the variability to account for different body parts being moreresponsive to weight loss than others. For example, a body part which ishighly responsive to weight loss would have a V value close to unity,while other less responsive body parts would have a greater than unity Vvalue. Table 1 shows the preferred values for V.

TABLE 1 Adjustment Variable for Each Body Part Body Part Skin Fold RankOrder Variable (V) neck  3 mm 9 1 biceps  4 mm 8 1.1 triceps 14 mm 6 1.5chest 21 mm 5 1.8 subscapula 19 mm 4 1.7 abdomen 30 mm 1 2.5 hip 24 mm 31.9 thigh 27 mm 2 2.0 calf 13 mm 7 1.2

The variable number is dependent on the skin fold of each body part. Therank order gives the ability to place the skin fold measurements in anorder that the variable members can be assigned. By ranking the skinfold measurements from greatest to least, the variable numbers can beassigned. This table can change depending on where a person stores theirfat.

Method of Producing the Predicted Image

In a preferred embodiment, the following method is implemented insoftware. The programming language is of little consequence, as therequired calculations can be performed by most well-known languages,including “C”, Java, and “C++.” The method comprises the steps of:

-   -   (a) Receive from intake data sheet all user information needed        for analysis (i.e., skin fold measurements, age, height, weight,        desired loss amount), and receive “before” photographic image        file.    -   (b) Scale real-life measurements to picture size.    -   (c) Place photograph on grid.    -   (d) Convert intake data to reduction on photograph utilizing        formula.    -   (e) Place locating grids of individual body parts on “before”        image.    -   (f) Find outline of the individual body parts within locating        grids.    -   (g) Apply reduction of the individual body parts using morphing        function.    -   (h) Apply original “before” photo next to reduced “after” photo.

In the first step, receipt of data from intake data sheet, the softwarereceives the user's name, age, current weight, height, and circumferencemeasurements for the neck, arm, chest, abdomen, hips, thigh, and calf.The data also includes skin fold measurements for the neck, biceps,triceps, chest, subscapula, abdomen, hips, thigh, and calf. The datafurther includes the desired percent body fat goal.

In the second step, the measurements are scaled to the picture size bytaking the person's height and dividing by picture height. Then, thisratio is multiplied by all other real life measurements to producescaled measurements. For example, if a person is actually 5 feet 9inches (69 inches), and the photograph submitted represents a 7 inchtall image, the scaling ratio is 69/7=9.8. So, all real-lifemeasurements would be multiplied by the inverse of the scaling ratio toyield a scaled measurement set.

In the third step, a locating grid is used to identify each body part,as shown in FIG. 4. Locating grids are placed on the arms (40), hipsand/or buttocks (42), abdomen (43), thigh (44), calf (45), chest (47),and neck (46). In a preferred embodiment, a feature extraction algorithmmay be used to automatically find each body portion, aided by theplacement of arrows (49) on a background behind the subject at the timeof taking the photograph. Alternatively, the body-part identifying gridsmay be placed on the photo manually through a graphical user interface.Both implementations are within the skill of the art of softwareengineers with expertise in this type of image processing.

In the fourth step, a grid is overlaid on each body segment image, asshown in FIG. 5. The grid (50) is useful in the process in finding theedges (51) of the image of the body part and in applying a percentreduction to the image.

In the fifth step, the reductions for each affected body segment areapplied using an image morphing function, as shown in FIG. 6 with thenew edges (60) of the body part image. This yields an “after” image inwhich each affected body part has been analytically reduced based uponeach part's responsiveness to fat loss, the estimated beginning fatlayer thickness based upon the skin fold measurements, and the desiredamount of reduction of fat.

Finally, simulated “before” (70) and “after” (71) images are displayedside-by-side for ease of comparison, such as shown in FIG. 7. Thus, amore accurate system and method are provided which scales the currentimage of the client or patient on a segmented basis using physiologicalcalculations.

Alternative Body Modeling Method

As an alternative to using actual photographic images of a person, asystem in accordance with the present invention may utilize a stock or“canned” image of a generic person that may be customized to representthe salient features of the particular user by specifying a set ofparameters. In this alternative embodiment, the present systempreferably models body transformation in terms of muscle gain as well asfat loss, and the model is preferably three-dimensional. In order tomake reasonable predictions, the system must begin with reasonableapproximations of how much fat and muscle a person has initially.Preferably, a person's height, weight, age, and percent body fat areused to determine this starting point. To present this informationvisually, the system translates these physiological attributes intounits that will cause the computer graphics model to present an imagethat is consistent with how one would expect a person with thoseattributes to look. Additionally, the system uses a description of thebody shape to know where on the body to place the fat. For example, ifthe person is “pear-shaped” (represented by a triangle), the model willshow most of the fat in the hips and thighs. If the person is shapedlike an “apple” (represented by a circle), the model will concentratethe fat in the middle of the body. Finally, if the person has a“straight” body shape (represented by a rectangle), the model willdistribute the fat more evenly. The overall effect is to create an imagethat is a good approximation of the person's initial appearance.

To build the body model, one of the first steps is preferably todetermine how many pounds (or kilos) of fat the person would have if heor she had an amount of muscle consistent with that of a person who iscurrently not exercising or lifting weights. In terms of a preferredcomputer model, this means that the muscle layer is turned off or zero.This will yield a reasonable initial estimate of the person's percentbody fat. If the user adjusts the percent body fat value, the systemwill modify the pounds (or kilos) of muscle and fat accordingly. Thesemodifications will be translated into adjustments in the computermodel's fat and muscle units. So, as the user increases the percent bodyfat, the model will grow “fatter,” and as the user reduces the percentbody fat, the model will grow more muscular.

The person's gender (male or female) is also generally an importantfactor because many relationships used in body modeling vary by gender.Additionally, the person's height and weight are important initialparameters. A set of equations is used to specify an optimal weight forany given height, depending on gender. By subtracting the optimal weightfrom the person's current weight, the system may establish an initialestimate of how many excess pounds of fat the person has available tolose. This determination of the amount of excess fat also enables thesystem to determine how much to inflate the fat layer of the model.

The optimal weight for a given height is given by the followingequations, in which the units are inches for height and pounds forweight.

Gender Height Condition Equation Male All Heights Ideal Weight = (4 ×Height) − 100 Female Height > 66 inches (5 ft Ideal Weight = (4 ×Height) − 131 6 in) Height < 66 inches (5 ft Ideal Weight = (3 × Height)− 65 6 in)These optimal weight equations are shown graphically in FIGS. 8 and 9.

In addition to the pounds of fat that can be lost through diet andexercise, there are some additional pounds of fat that are required bythe human body. To estimate the percentage of body fat for a person, onemust estimate essential fat as well as excess fat. By combining theestimated excess pounds of fat and the estimated essential pounds offat, one arrives at an estimate of the total pounds of fat.

A person's amount of essential fat varies by age and may be estimated bythe following equation:Essential Fat=((Age×0.001625)+0.0425)(Ideal Weight)This essential fat equation is illustrated graphically in FIG. 10.

To calculate the estimated body fat percentage, divide the pounds of fatby the person's current body weight as follows:Body Fat Percentage=(Essential Fat+Excess Fat)/Body WeightIf the person is female, add 3%.

Preferably, a computer program in accordance with the present inventionwill not render an initial estimate of body fat percentage that is lessthan 4% for males or less than 7% for females, and the maximum estimatedbody fat percentage is preferably 61% for males and 64% for females.

Thus, by way of example, for a male who is 6 feet (72 inches) in height,240 pounds in weight, and 40 years of age, his initial body fat may beestimated using the above equations as follows:Ideal Weight=(4×72 Inches)−100=188 Lbs.Excess Pounds=240 Lbs.−188 Lbs.=52 Lbs.Essential Fat=((40 Yrs)×(0.001625)+0.0425)×(188 Lbs)=20.21 Lbs.Pounds of Fat=20.21 Lbs.+52 Lbs.=72.21 Lbs.Percent Body Fat=72.21 Lbs./240 Lbs.=30%A preferred display screen corresponding to the foregoing example isshown in FIG. 17. The person's height, weight, age, and body fat arepreferably selected by slider bars or entered in dialog boxes as shownin FIG. 17, but such parameters may be entered in any other suitablemanner.

A person's body appearance as well as the amount of fat for any givenweight will vary depending on the person's age. The person's age ispreferably used to make a small adjustment to the amount of fat thatappears on the model for any given weight. This variation is preferablydescribed by a parabolic function that applies between the ages of 30and 70 as reflected in the following equation for males, which isdepicted graphically in FIG. 11:Age Factor=((−0.000438)Age²+(0.0439)Age)−1A similar age factor relationship for females is depicted graphically inFIG. 12. For men, this age factor is preferably applied to boost fatpounds by up to 10% of a person's ideal weight. For women, the agefactor is preferably applied to boost fat pounds by up to 5% of theirideal weight. For a middle-aged person, these additional pounds of fatwill affect the appearance of the model. However, these additionalpounds preferably are not assumed to be available for loss.

Thus, by way of example, considering again a 6 foot tall male who is 40years of age, the weight adjustment for age is preferably calculated asfollows:Age Factor=((−0.000438)(40)²+(0.0439)(40))−1=0.0552Age Pounds=188(0.0552)=10.3776 lbs.This man's body model will then be shown with 10 more pounds of fat thanwould a similar person of age 35.

A system in accordance with the present invention also preferably allowsmanual adjustment of the user's body fat percentage. After an initialestimate of body fat percentage has been calculated, the user mayincrease or decrease the body fat percentage by sliding the “% Body Fat”adjuster right or left (see FIGS. 17-19). This will have the effect ofmaking the model “flabbier” or more muscular, as desired. When the userincreases the body fat percentage, the software program increases thepounds of fat assumed for the given weight to reflect the newpercentage. This also increases the number of pounds of fat availablefor loss. The fat layer of the model is inflated to reflect theseadditional pounds of fat.Pounds of Muscle Converted into Fat=(Body Weight)(Selected Body Fat%−Initial Body Fat %)

In the previous example, the 6 foot, 240 pound, 40 year old male wasestimated to have a body fat percentage of 30%. If the user selects abody fat percentage of 37%, the model will reflect 16.8 more pounds ofexcess fat, as follows (see FIG. 18):Pounds of Muscle Converted into Fat=(240)(37%−30%)=16.8 Lbs.

If the user reduces the body fat percentage, the program converts poundsof fat (that were available for loss) into lean muscle mass to reflectthe new percentage. As lean muscle is added, the muscle layer of themodel is inflated to show greater muscle development. Additionally, thefat layer is reduced to reflect fewer pounds of excess fat.Pounds of Fat Converted into Muscle=(Body Weight)×(Initial Body Fat%−Selected Body Fat %)

In the previous example the 6 foot, 240 pound, 40 year old male wasestimated to have a body fat percentage of 30%. If the user selects abody fat percentage of 25%, the model will reflect 12 more pounds oflean muscle and 12 less pounds of fat, as follows (see FIG. 19):Pounds of Fat Converted into Muscle=(240)×(30%−25%)=12 Lbs.

In addition, the muscle layer of the model and the fat layer of themodel preferably may be adjusted independently by sliding the MuscleAdjustment or the Fat Adjustment slider bars. Increasing the MuscleAdjustor increases the appearance of muscle without modifying the weightor body fat percentage of the subject. Similarly, moving the FatAdjustment will change the amount of fat displayed on the model withoutchanging the weight or the pounds of fat available for loss. Movingthese sliders modifies the default assumptions about how a pound of fator a pound of muscle appears on a particular person. All subsequentmodifications to the appearance of the model for this person willreflect these manual adjustments.

Health Risk Calculations

The present system also preferably estimates the risk of Type IIDiabetes, Cardiovascular Heart Disease, and Stroke. Such disease risksare preferably calculated based on Body Mass Index (BMI), body fatpercentage, and family history of disease. Age, gender, smoking,exercise, high blood pressure, and high cholesterol are also preferablytaken into account. The calculated risk values are based on assumptionsabout the relative importance of several well known risk factors. Theestimates of the magnitudes of each risk factor attempt to capture theprevailing wisdom about Type II Diabetes, Cardiovascular Heart Disease,and Stroke. Although quantifying a person's risk with a number in thismanner does not imply the person's actual risk (rather, it indicatesonly that their risk could be higher or lower if certain factors weredifferent), these calculations dramatically demonstrate how somebehaviors increase disease risk while others reduce a person's risk ofdisease.

As shown in FIG. 20, the person's BMI is preferably displayed with avertical progress bar. The range of the BMI bar is preferably 18 to 40.Disease risk factors are preferably displayed with vertical progressbars having a range of 0 to 100. The level of risk is expressed by avalue between 0 and 100, which may be qualitatively categorized asfollows:

Risk Value Risk Category  0-30 Minimal 31-39 Low 40-52 Moderate 53-75High 76-98 Very High Over 98 Extremely High

The disease risk calculations preferably take into account the effectsof body mass and body fat percentage on a person's health. The obesityfactor is a combination of a person's BMI and a measure of their excessbody fat. Excess body fat depends on a person's age and gender. Thefollowing table shows an acceptable amount of body fat for persons ofvarious ages:

Age 20 40 60 80 Male 14 15 17 18 Female 17 18 19 21

To determine the excess body fat, the program subtracts the acceptablebody fat percentage from the person's actual body fat percentage.

The obesity factor is preferably constructed from these basic functions:Body Mass Index(BMI)=(Weight×704.5)/Height×HeightBMI Factor=(BMI−25)×7.5Acceptable Body Fat=(Age×0.0667−1.3333)+14; (add 3 more if female)Excess Body Fat Factor=% Body Fat−Acceptable Body FatThe obesity factor is an average of the BMI factor and the Excess BodyFat Factor:Obesity Factor=(BMI Factor+Excess Body Fat Factor)/2.Constructing the obesity factor in this way allows the present system touse the power of BMI to predict disease while also taking into accountthe effect of a high body fat percentage. The obesity factor ispreferably scaled by the function:Y=0.04X−0.28

This scaling function effectively lowers the calculated risks forathletes and others with a low percentage of body fat. If the BMI effectis a proxy for the amount of body fat a person carries, then if theperson's body fat is known to be lower, then the effect of this factorshould be smaller. For example, using this scaling function, the obesityfactor for a person with 7% body fat would be scaled to 0. However, theobesity factor of a person with 32% body fat would be scaled by 100%.

Diabetes Risk Calculation

A person's risk of Type II Diabetes is the sum of six factors: familyhistory, exercise, age, gender, BMI, and Percent Body Fat.

Family History

-   -   Add 15 if the subject has a family history of diabetes.

Exercise

-   -   Add 25 if the subject is not exercising.

Age

-   -   Add (Age/5)−7

BMI

-   -   Add 7.5 for every unit of BMI over 25%

% Body Fat

-   -   Add 1 for every percent of Excess Body Fat    -   Scales obesity factor by (4X−28)%        The obesity factor is magnified by 25% for Type II Diabetes.

By way of example, consider a female subject 5 ft. 6 in. tall, 175 lbs.,36 years old who has a family history of diabetes, does not exercise,has a BMI of 28.3, and has a body fat percentage of 35%. Her risk ofDiabetes is calculated as follows (see FIG. 21):

Family History of diabetes {0, 15} 15 Not Exercising {0, 25} 25 Age(36/5) − 7 = 0 0 BMI Factor 7.5 × (28.3 − 25)) = 24.75 Acceptable BodyFat 17 + (36 × 0.07) − 1.3 = 18 Excess Body Fat Factor 35 − 18 = 17Obesity Factor ((24.75) + 17))/2 = 20.9 Body Fat Scaler 4(35) − 28 =112% × 20.9 = 23.4 25% Magnifier for Diabetes 1.25 × 23.4 = 29 DiabetesRisk 69A value of 69 places the subject in the “High” risk category, asdepicted in FIG. 21.

Heart Disease Risk Calculation

Coronary Heart Disease Risk is the sum of nine factors: age, familyhistory, gender, smoking, high blood pressure, exercise, BMI, and bodyfat percentage.

Age:

-   -   Add 1 for each year over 59 up to a maximum of 10 years.

Family History:

-   -   Add 15 if the subject has a family history of heart disease.    -   Add 5 if the subject has a family history of Diabetes.

Smoking

-   -   Add 25 if the subject smokes.

Gender

-   -   Add 5 if the subject is male.

High Blood Pressure

-   -   Add 15 if the subject has high blood pressure (greater than        120/80).

Exercise

-   -   Add 10 if the subject is not exercising.

High Cholesterol

-   -   Add 10 for high cholesterol (240 mg/dL or higher).

BMI

-   -   Add 7.5 for every unit of BMI over 25.

% Body Fat

-   -   Add 1 for every percent of Excess Body Fat.    -   Scales Obesity factor by (4X−28)%

As an example, consider a male subject 6 ft. tall, 250 lbs., 62 yearsold who smokes and has a family history of heart disease. He does nothave high blood pressure or high cholesterol. His BMI is 34.0. His BodyFat is 36%. His risk of heart disease is calculated as follows (see FIG.22):

Age over 59: 62 − 59 = 3 3 Family History of heart disease {0, 15} 15Family History of diabetes {0, 5} 0 Smoking {0, 25} 25 Male Gender {0,5} 5 No High Blood Pressure {0, 15} 0 Some Exercise {0, 10} 0 HighCholesterol {0, 10} 0 BMI Factor 7.5 × (34.0 − 25)) = 67.5 AcceptableBody Fat 14 + (62 × 0.07) − 1.3 = 17 Excess Body Fat Factor 36 − 17 = 19Obesity Factor ((67.5) + 19))/2 = 43.25 Body Fat Scaler 4(36 − 28 = 116%× 43.25 = 50 Heart Disease Risk 98A value of 98 places the subject in the “Very High” risk category, asdepicted in FIG. 22.

Stroke Risk Calculation

Stroke risk is the sum of eight factors: age, family history, gender,smoking, high blood pressure, exercise, BMI, and high cholesterol.

Age:

-   -   Add 1 for each year over 59.

Family History:

-   -   Add 15 if the subject has a family history of stoke.    -   Add 5 if the subject has a family history of diabetes.

Smoking

-   -   Add 15 if the subject smokes.

Gender

-   -   Add 5 if the subject is male.

High Blood Pressure

-   -   Add 25 if the subject has high blood pressure (greater than        120/80).

Exercise

-   -   Add 5 if the subject is not exercising.

High Cholesterol

-   -   Add 10 for high cholesterol (240 mg/dL or higher)

BMI

-   -   Add 7.5 for every unit of BMI over 25%

% Body Fat

-   -   Add 1 for every percent of Excess Body Fat    -   Scales Obesity factor by (4X−28)%

As an example of stroke risk calculation, consider a female subject 5ft. 3 in. tall, 165 lbs., 60 years old who smokes, has high bloodpressure, is physically inactive, and has a family history of stroke.Her BMI is 29.3. Her Body Fat is 38%. Her risk of stroke is calculatedas follows (see FIG. 23):

Age over 59: 60 − 59 = 1 1 Family History of Stoke {0, 15} 15 FamilyHistory of diabetes {0, 5} 0 Smoking {0, 15} 15 Male Gender {0, 5} 0High Blood Pressure {0, 25} 25 Not Exercising {0, 5} 5 High Cholesterol{0, 10} 0 BMI Factor 7.5 × (29.3 − 25)) = 32.25 Acceptable Body Fat 17 +(60 × 0.07) − 1.3 = 20 Excess Body Fat Factor 38 − 20 = 18 ObesityFactor ((32.25) + 18))/2 = 25.1 Body Fat Scaler 4(38) − 28 = 124% × 25.1= 31 Risk of Stroke 92A value of 92 places the subject in the “Very High” risk category, asdepicted in FIG. 23.Fat Loss Calculations

To predict how much fat will be burned over time, the present systemtreats the human body as a system that is constantly consuming energy.If a person takes in more energy (food) than the body can use, thatsurplus energy will be stored as fat. If a person uses more energy thanis consumed, the body will convert stored fat into energy and thatperson will lose fat. A person can create an energy deficit by eatingless or exercising more. It is this energy deficit that the presentsystem calculates. When a user selects a certain number of days ofcompliance per week (or other suitable period of time) for eating andexercising according to plan, the present system adds up their caloricdeficit for the week (or other suitable time period). According to wellknown data, it is assumed that one pound of fat is equal to 3,500calories of energy. So, if a person can introduce a 3,500-caloriedeficit within the course of a week, that person will lose one pound inone week.

It is assumed that the person's current physical condition is a resultof their current eating and exercise habits. To recommend a dietarycaloric deficit, the system first calculates how many calories arerequired per day to maintain the person's current weight. From thatmaintenance amount, subtracting an appropriate number of caloriescreates the desired dietary deficit. To find the initial maintenancevalue, the system preferably uses the standard calculation for BasilMetabolic Rate (BMR), as follows.BMR_(female)=ActivityRate×(665.1+(9.56×Weight)+(1.85×Height)−(4.68×Age));BMR_(male)=Activity Rate×(66.47+(13.75×Weight)+(5.0×Height)−(6.76×Age)).

An Activity Rate of 1.3 (ambulatory) is preferably used to account foronly their normal, daily activity. The effects of the exercise regimenare added separately. For example, a dietary deficit of 500 calories perday is found by subtracting 500 from the calculated BMR.Calories per Day=BMR−500 calories

The present system provides three basic ways to create a caloricdeficit: diet, resistance training, and cardiovascular training.Additionally, the present system allows the user to take into accountthe effects of dietary supplements and personal training in boosting thenumber of calories burned. The number of calories burned per week is thesum of five products:

Calories  Burned/Week = (Daily  Dietary  Deficit  calories × Days  on  Diet) + (Cardiovascular  Exercise  session  calories × Days  of  Cardiovascular  Exercise) + (Resistance  Training  session  calories × Days  of  Resistance  Exercise) + (Caloric  value  of  a  day  of  Supplementation × Days  of  Supplementation) + (Caloric  boost  value  of  a  day  of  Personal  Assistance × Days  of  Personal  Assistance)The pounds of fat burned per week is found by dividing the CaloriesBurned per Week by the constant for Calories burned per Pound, asfollows:Pounds of Fat Burned/Week=(Calories of Fat Burned/Week)/(Calories/Pound)

The caloric value of a resistance training session or a cardiovascularexercise session varies with the intensity of the workout. Therefore,the value used in the calculations will vary depending on the type ofexercise program selected. The user can vary the values used in thesecalculations by selecting different goals. In addition to the boostexperienced while the personal trainer is present, the intensity of allsolitary resistance training sessions is assumed to increase by somesmall amount.

As an example, consider the following fitness program, which isgraphically depicted in FIG. 24.

For the selected goal, the preferred caloric values used are as shown inthe below table.

Days of Caloric Total Activity Compliance Contribution Calories DailyDietary Deficit = 500 5 Days/Week 2500 calories Calories Caloric Valueof 4 Days/Week 1200 calories Cardiovascular Exercise session = 300calories Caloric Value of Resistance 3 Days/Week  975 calories Trainingsession (boosted by personal assistance) = 325 calories Caloric Value ofDietary 5 Days/Week  500 calories Supplementation = 100 calories CaloricValue of Personal 2 Days/Week  100 calories Assistance = 50 calories(while trainer is present) Total Weekly Caloric 5275 calories DeficitThe fat burned as a result of this regimen is then calculated asfollows:Fat Burned=5275/3500=1.5 Lbs/WeekGiven a rate of pounds burned per week, it is possible to calculate thenumber of pounds burned over a given period of time.Lbs=Weeks×Lbs/Week

For example,Lbs. of Fat Burned in 26 Weeks=26 Weeks×1.5 Lbs/Week=39 Lbs.

It is also possible to calculate how many weeks will be required to burna particular amount of fat:Weeks to burn 39 Lbs.=39 Lbs./1.5=26 Weeks

The present system will preferably calculate Weeks or Pounds, dependingon which slider bar is “grabbed” by the user. If the user slides the“Pounds of Fat Burned” slider, the program will use the rate of fat burnto adjust the “Goal Timeline” Slider bar. Conversely, if the user slidesthe “Goal Timeline” slider bar, pounds of fat burned will be calculated.In either event, the body image will be slimmed to reflect the pounds offat lost, as shown in FIG. 25. Also, the percent body fat display willbe updated to reflect the new body composition.

The present system will preferably predict weight loss based on excesspounds of fat only. As described above, a person is assumed to have anoptimal weight for any given height. Once all of the excess fat poundsare “lost,” continued fat loss calculations are stopped. The presentsystem preferably will not recommend weight loss below the assumedoptimal weight, unless the person's body fat percentage indicates thatadditional excess fat pounds remain to be burned. Additionally, “ageappearance adjustments,” as discussed above, preferably remain ineffect.

Muscle Gain Calculations

The present system preferably predicts muscle gain based on the person'sage and gender as well as the muscle building program selected. Thecomponents affecting a muscle building program are nutrition, resistancetraining, and dietary supplementation. Preferably, it is assumed thatthe maximum amount of muscle will be gained by an 18 year old male. Theamount of muscle gain will depend upon the number of days of resistancetraining, proper nutrition, and supplementation. The present systempreferably calculates muscle gain in units that are native to the bodymodel. The calculations are scaled to cause the body model's muscle toinflate with a rate that is consistent with what one would expect givena particular fitness program, and preferably in uniform proportions. Theresulting muscle gain is transformed into pounds to display the poundsof muscle gained.

Base Muscle Gain Factor

The Base Muscle Gain factor represents the change in the appearance ofthe model after one resistance training session, if all other factorsare at maximum. The magnitude of this factor changes depending on thegoal selected by the user. For example, if the person's goal is to loseweight, the nutritional recommendations favor fat loss at the expense ofmuscle gain. However, if the person's selected goal is to gain muscle,the nutritional recommendations will favor muscle gain. Therefore, themagnitude of the Base Muscle Gain factor will be greater if one's goalis to gain muscle. Preferred Base Muscle Gain factors for various goalsare listed in the table below.

Goal Base Muscle Gain Factor (model units) Muscle Gain 1/725  MuscleGain and Fat Loss 1/1087 Fat Loss 1/1450 Health/Maintenance 1/1450

Supplementation Boost

Supplements are preferably assumed to boost muscle formation, dependingon the number of days of supplementation and the number of days ofresistance training. The default value for the supplement boost factoris preferably 15%.

Supplement  Boost = 1.0 + ((Days  of  Resistance  Training/7  days) × (Days  of  Supplemenation/7  days) × Supplement  Boost  Factor)

Resistance Training Compliance

As illustrated in FIG. 13, the effect of the number of days ofresistance training is preferably assumed to be linear for up to 4 daysper week. However, the effect of days 5, 6, and 7 are reduced by afunction that compensates for the lack of rest days. Accordingly,Resistance Compliance is defined as follows:

If Days of Resistance training is greater than 4:Resistance Compliance=(Days of Resistance Training/3)+2.56667OtherwiseResistance Compliance=Days of Resistance Training

Scaling Muscle Gain by Age

A 40 year old man, for instance, cannot build as much muscle in a givenamount of time as he could have when he was 18, even though he engagesin an identical fitness program. Therefore, the present systempreferably scales the muscle response according to age, which ispreferably accomplished in two steps. The first step preferably uses afunction that peaks at age and has a minimum value of 0.1, as shown inFIG. 14 and defined by the following equation. This function allocatesthe amount of muscle available for gain by age.Age Compensation=Age×−0.015909+1.286364

A second function preferably transforms the scaling from model unitsinto pounds gained according to the following equation:Age Scaler=Age×−0.618182+65.7273

Combining the two functions yields the preferred overall muscle gain byage curve as illustrated in FIG. 15 and defined by the followingequation:Age Factor=Age²(0.009835)+Age(−1.84086)+84.54923

Scaling Muscle Gain by Nutritional Compliance

Proper nutrition is required to achieve maximum muscle gain. Therefore,days of inadequate nutrition reduce the amount of muscle gained. ANutrition Factor is therefore preferably calculated by the followingequation, which is depicted graphically in FIG. 16:Nutrition Factor=Days/Week on Nutrition Plan(0.035714286)+0.75

Scaling Muscle Gain for Gender

Females are preferably assumed to be able to develop 55% as much muscleas males. For example, if a male can develop 11 pounds of muscle overone year on a particular fitness program, a female will develop 6 poundsover the same time using an identical fitness program. Accordingly, aGender Factor is preferably established as follows:Gender Factor_(female)=0.55Gender Factor_(male)=1.0

Combined Scaling for Muscle Gain

As the user slides the Goal Timeline slider bar to the right, thepresent system calculates how much muscle is developed according to thefitness program selected, and the model image becomes more muscular. Todetermine how many pounds of muscle have been gained, the above scalefactors are preferably multiplied together to determine the rate ofmuscle gain as follows:

Muscle  Gained/Week = (Resistance  Compliance × Base  Muscle  Gain  Factor) × Supplement  Boost × Age  Factor × Nutrition  Factor × Gender  FactorThe pounds of muscle gained over a number of weeks are found bymultiplying the rate of gain by the time in weeks, as follows:Muscle Gained in Lbs=(Muscle Gained in Lbs/Week)×Weeks

As an example of a muscle gain calculation, consider a 220 pound, 40year old male who eats properly 5 days per week, takes supplementation 6days per week, and lifts weights 5 days per week as illustrated in FIG.26. His primary goal is to gain muscle. To determine his muscle gain,the system first calculates all of the relevant scaling factors asfollows:Base Muscle Gain Factor=1/725Supplement Boost=1.0+((5/7 days)×(6/7 days)×0.15)=1.09Resistance Compliance=(5 days/3)+2.56667=4.2333Age Factor=(40 yrs)²(0.009835)+(40 yrs)(−1.84086)+84.54923=26.6508Nutrition Factor=(5 days)(0.035714286)+0.75=0.9286Gender Factor_(male)=1.0Those factors are then combined to find the rate of muscle gain inpounds as follows:

$\begin{matrix}{{{Muscle}\mspace{14mu}{Gained}\text{/}{Week}} = {\left( {4.2333/725} \right) \times (1.09) \times (26.6508) \times}} \\{(0.9286) \times (1.0)} \\{= 0.1575}\end{matrix}$Finally, the number of weeks is multiplied by the muscle gain rate tofind the total number of pounds of muscle gained as follows:Muscle Gained(lbs)=(0.1575 Lbs/Week)×(52 Weeks)=8 Lbs.The pounds of muscle gained are preferably displayed for the user on adisplay screen showing before and after images as illustrated in FIG.26.Body Fat Percentage Recalculation

After a user has engaged in a fitness program over a period of time, thepresent system's body model composition will preferably change toreflect a loss of fat and/or a gain in muscle. Therefore, the body fatpercentage should be recalculated. To recalculate the body fatpercentage, the system should first determine the person's new bodyweight. The new body weight is found by subtracting the pounds of fatlost and adding the muscle gained as follows:Weight_(new)=Weight_(initial)−Fat Lost+Muscle Gained(Lbs)

The system should also determine the pounds of body fat remaining. To dothis, the known or assumed percent body fat of the person is used todetermine how many pounds of fat the person currently has as follows:Fat_(initial)(Lbs)=Body Fat Percentage_(initial)×Weight_(initial)(Lbs)The remaining pounds of fat are determined by subtracting the pounds offat lost from the original pounds of body fat as follows:Fat_(remaining)=Fat_(initial)−Fat Lost(Lbs)The new body fat percentage is found by dividing the pounds of fatremaining by the new body weight as follows:Body Fat_(new)(%)=Fat_(remaining)÷Weight_(new)

As an example, consider a 180 pound female with an initial body fatpercentage of 35% as illustrated in FIG. 27. After engaging in a fitnessprogram for 6 months, she loses 30 pounds of fat and gains 3 pounds ofmuscle. Her new weight is 153 Lbs.Weight_(new)=(180 Lbs)−(30 Lbs)+(3 Lbs)=153 Lbs.Her initial pounds of fat were 63 Lbs.Fat_(initial)(Lbs)=Bo(35%)×180(Lbs)=63 Lbs.Her remaining pounds of fat are 33 Lbs.Fat_(remaining)=(63 Lbs)−(30 Lbs)=33 Lbs.Therefore, her new percent body fat is 22%, as illustrated in FIG. 28.Body Fat_(new)(%)=(33 Lbs)/(153 Lbs)=22%

Although the foregoing specific details describe a preferred embodimentof this invention, persons reasonably skilled in the art will recognizethat various changes may be made in the details of this inventionwithout departing from the spirit and scope of the invention as definedin the appended claims. Therefore, it should be understood that thisinvention is not to be limited to the specific details shown anddescribed herein.

What is claimed is:
 1. A computer-implemented method of creatingindications of health risk and personal appearance for a person,comprising the following being performed by a computer: receiving a dataset associated with said person in a pre-regimen condition, said dataset comprising: a weight, a height, an age, a gender designation, adesignation regarding family history of diabetes, and a designationregarding the person's level of exercise; calculating a health risk ofdiabetes for said person in said pre-regimen condition based on saiddata set; receiving or creating a first image representative of saidperson in said pre-regimen condition; receiving a second data setassociated with said person in a post-regimen condition, said seconddata set comprising a selected compliance with respect to a regimen ofdiet, exercise, or diet and exercise; calculating a predicted healthrisk of diabetes for said person in said post-regimen condition based onsaid second data set; creating a second image predictive of said personin said post-regimen condition based on said second data set; andgenerating a screen display suitable for displaying on a computerscreen, said screen display comprising said first image, a firstindication of said health risk of diabetes for said person in saidpre-regimen condition associated with said first image, said secondimage, and a second indication of said predicted health risk of diabetesfor said person in said post-regimen condition associated with saidsecond image; said method further comprising: calculating the person'sbody mass index (BMI) according to the equationBMI=(Weight×704.5)/Height×Height; calculating the person's BMI factoraccording to the equationBMI Factor=(BMI−25)×7.5; calculating the person's acceptable body fataccording to the equationAcceptable Body Fat=(Age×0.0667−1.3333)+14 (if said person is male);Acceptable Body Fat=(Age×0.0667−1.3333)+17 (if said person is female);calculating the person's excess body fat factor according to theequationExcess Body Fat Factor=% Body Fat−Acceptable Body Fat; calculating theperson's obesity factor according to the equationObesity Factor=(BMI Factor+Excess Body Fat Factor)/2; scaling saidobesity factor according to the equationBody Fat Scaler=[4×(% Body Fat)−28]×(Obesity Factor); magnifying saidBody Fat Scaler according to the equationMagnifier=1.25×Body Fat Scaler; assigning a family history value, saidfamily history value being equal to zero if said person has no familyhistory of diabetes, said family history value being equal to 15 if saidperson has a family history of diabetes; assigning an exercise value,said exercise value being equal to zero if said person is exercising,said exercise value being equal to 25 if said person is not exercising;assigning an age value according to the equationAge value=(Age/5)−7 (if (Age/5)−7 is greater than or equal to zero)Age value=0 (if (Age/5)−7 is less than zero); calculating said risk ofdiabetes according to the equationDiabetes Risk=Family History Value+Exercise Value+Age Value+Magnifier.2. A computer-implemented method of creating indications of health riskand personal appearance for a person, comprising the following beingperformed by a computer: receiving a data set associated with saidperson in a pre-regimen condition, said data set comprising: a weight, aheight, an age, a gender designation, a designation regarding familyhistory of heart disease, a designation regarding family history ofdiabetes, a designation regarding the person's level of exercise, adesignation regarding the person's blood pressure, a designationregarding the person's smoking activity, and a designation regarding theperson's cholesterol level; calculating a health risk of heart diseasefor said person in said pre-regimen condition based on said data set;receiving or creating a first image representative of said person insaid pre-regimen condition; receiving a second data set associated withsaid person in a post-regimen condition, said second data set comprisinga selected compliance with respect to a regimen of diet, exercise, ordiet and exercise; calculating a predicted health risk of heart diseasefor said person in said post-regimen condition based on said second dataset; creating a second image predictive of said person in saidpost-regimen condition based on said second data set; and generating ascreen display suitable for displaying on a computer screen, said screendisplay comprising said first image, a first indication of said healthrisk of heart disease for said person in said pre-regimen conditionassociated with said first image, said second image, and a secondindication of said predicted health risk of heart disease for saidperson in said post-regimen condition associated with said second image;said method further comprising: calculating the person's body mass index(BMI) according to the equationBMI=(Weight×704.5)/Height×Height; calculating the person's BMI factoraccording to the equationBMI Factor=(BMI−25)×7.5; calculating the person's acceptable body fataccording to the equationAcceptable Body Fat=(Age×0.0667−1.3333)+14 (if said person is male);Acceptable Body Fat=(Age×0.0667−1.3333)+17 (if said person is female);calculating the person's excess body fat factor according to theequationExcess Body Fat Factor=% Body Fat−Acceptable Body Fat; calculating theperson's obesity factor according to the equationObesity Factor=(BMI Factor+Excess Body Fat Factor)/2; scaling saidobesity factor according to the equationBody Fat Scaler=[4×(% Body Fat)−28]×(Obesity Factor); assigning an agevalue according to the equationAge Value=Age−59 (if (Age−59) is greater than or equal to zero)Age Value=0 (if (Age−59) is less than zero); assigning a heart diseasefamily history value, said heart disease family history value beingequal to zero if said person has no family history of heart disease,said heart disease family history value being equal to 15 if said personhas a family history of heart disease; assigning a diabetes familyhistory value, said diabetes family history value being equal to zero ifsaid person has no family history of diabetes, said diabetes familyhistory value being equal to 5 if said person has a family history ofdiabetes; assigning a smoking value, said smoking value being equal tozero if said person is not smoking, said smoking value being equal to 25if said person is smoking; assigning a gender value, said gender valuebeing equal to zero if said person is female, said gender value beingequal to 5 if said person is male; assigning a blood pressure value,said blood pressure value being equal to zero if said person does nothave high blood pressure, said blood pressure value being equal to 15 ifsaid person has high blood pressure; assigning an exercise value, saidexercise value being equal to zero if said person is exercising, saidexercise value being equal to 10 if said person is not exercising;assigning a cholesterol value, said cholesterol value being equal tozero if said person does not have high cholesterol, said cholesterolvalue being equal to 10 if said person has high cholesterol; calculatingsaid risk of heart disease according to the equationHeart  Diease  Risk = Age  Value + Heart  Diease  Family  History  Value + Diabetes  Family  History  Value + Smoking  Value + Gender  Value + Blood  Pressure  Value + Exercise  Value + Cholesterol  Value + Body  Fat  Scaler.3. A computer-implemented method of creating indications of health riskand personal appearance for a person, comprising the following beingperformed by a computer: receiving a data set associated with saidperson in a pre-regimen condition, said data set comprising: a weight, aheight, anan age, a gender designation, a designation regarding familyhistory of stroke, a designation regarding family history of diabetes, adesignation regarding the person's smoking activity, a designationregarding the person's level of exercise, a designation regarding theperson's blood pressure, and a designation regarding the person'scholesterol level; calculating a health risk of stroke for said personin said pre-regimen condition based on said data set; receiving orcreating a first image representative of said person in said pre-regimencondition; receiving a second data set associated with said person in apost-regimen condition, said second data set comprising a selectedcompliance with respect to a regimen of diet, exercise, or diet andexercise; calculating a predicted health risk of stroke for said personin said post-regimen condition based on said second data set; creating asecond image predictive of said person in said post-regimen conditionbased on said second data set; and generating a screen display suitablefor displaying on a computer screen, said screen display comprising saidfirst image, a first indication of said health risk of stroke for saidperson in said pre-regimen condition associated with said first image,said second image, and a second indication of said predicted health riskof stroke for said person in said post-regimen condition associated withsaid second image; said method further comprising: calculating theperson's body mass index (BMI) according to the equationBMI=(Weight×704.5)/Height×Height; calculating the person's BMI factoraccording to the equationBMI Factor=(BMI−25)×7.5; calculating the person's acceptable body fataccording to the equationAcceptable Body Fat=(Age×0.0667−1.3333)+14 (if said person is male);Acceptable Body Fat=(Age×0.0667−1.3333)+17 (if said person is female);calculating the person's excess body fat factor according to theequationExcess Body Fat Factor=% Body Fat−Acceptable Body Fat; calculating theperson's obesity factor according to the equationObesity Factor=(BMI Factor+Excess Body Fat Factor)/2; scaling saidobesity factor according to the equationBody Fat Scaler=[4×(% Body Fat)−28]×(Obesity Factor); assigning an agevalue according to the equationAge Value=Age−59 (if (Age−59) is greater than or equal to zero)Age Value=0 (if (Age−59) is less than zero); assigning a stroke familyhistory value, said stroke family history value being equal to zero ifsaid person has no family history of stroke, said stroke family historyvalue being equal to 15 if said person has a family history of stroke;assigning a diabetes family history value, said diabetes family historyvalue being equal to zero if said person has no family history ofdiabetes, said diabetes family history value being equal to 5 if saidperson has a family history of diabetes; assigning a smoking value, saidsmoking value being equal to zero if said person is not smoking, saidsmoking value being equal to 15 if said person is smoking; assigning agender value, said gender value being equal to zero if said person isfemale, said gender value being equal to 5 if said person is male;assigning a blood pressure value, said blood pressure value being equalto zero if said person does not have high blood pressure, said bloodpressure value being equal to 25 if said person has high blood pressure;assigning an exercise value, said exercise value being equal to zero ifsaid person is exercising, said exercise value being equal to 5 if saidperson is not exercising; assigning a cholesterol value, saidcholesterol value being equal to zero if said person does not have highcholesterol, said cholesterol value being equal to 10 if said person hashigh cholesterol; calculating said risk of stroke according to theequationStroke  Risk = Age  Value + Stroke  Family  History  Value + Diabetes  Family  History  Value + Smoking  Value + Gender  Value + Blood  Pressure  Value + Exercise  Value + Cholesterol  Value + Body  Fat  Scaler.